Abstract
Videos from the World Wide Web provide a rich source of information that robots could use to acquire knowledge about manipulation tasks. Previous work has focused on generating action sequences from unconstrained videos for a single robot performing manipulation tasks by itself. However, robots operating in the same physical space with people need to not only perform actions autonomously, but also coordinate seamlessly with their human counterparts. This often requires representing and executing collaborative manipulation actions, such as handing over a tool or holding an object for the other agent. We present a system for knowledge acquisition of collaborative manipulation action plans that outputs commands to the robot in the form of visual sentence. We show the performance of the system in 12 unlabeled action clips taken from collaborative cooking videos on YouTube. We view this as the first step towards extracting collaborative manipulation action sequences from unconstrained, unlabeled online videos.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yang, Y., Li, Y., Fermüller, C., Aloimonos, Y.: Robot learning manipulation action plans by “watching” unconstrained videos from the world wide web. In: AAAI, pp. 3686–3693 (2015)
Das, P., Xu, C., Doell, R.F., Corso, J.J.: A thousand frames in just a few words: lingual description of videos through latent topics and sparse object stitching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2634–2641 (2013)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Simon, T., Joo, H., Matthews, I., Sheikh, Y.: Hand keypoint detection in single images using multiview bootstrapping. In: CVPR (2017)
Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008 (2018)
Yang, Y., Guha, A., Fermuller, C., Aloimonos, Y.: A cognitive system for understanding human manipulation actions. Adv. Cogn. Syst. 3, 67–86 (2014)
Chelba, C., et al.: One billion word benchmark for measuring progress in statistical language modeling. In: INTERSPEECH 2014. 15th Annual Conference of the International Speech Communication Association, pp. 2635–2639 (2014)
Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)
Zhang, H., Heiden, E., Nikolaidis, S., Lim, J.J., Sukhatme, G.S.: Auto-conditioned recurrent mixture density networks for learning generalizable robot skills. CoRR abs/1810.00146 (2018). http://arxiv.org/abs/1810.00146
Mandlekar, A., et al.: ROBOTURK: a crowdsourcing platform for robotic skill learning through imitation. In: 2nd Annual Conference on Robot Learning, CoRL 2018, pp. 879–893 (2018)
Malmaud, J., Huang, J., Rathod, V., Johnston, N., Rabinovich, A., Murphy, K.: What’s cookin’? Interpreting cooking videos using text, speech and vision. CoRR abs/1503.01558 (2015). http://arxiv.org/abs/1503.01558
Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: a survey. IEEE Trans. Circ. Syst. Video Technol. 18(11), 1473 (2008)
Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R.J., Saenko, K.: Translating videos to natural language using deep recurrent neural networks. In: NAACL HLT 2015, pp. 1494–1504 (2015)
Nguyen, A., Kanoulas, D., Muratore, L., Caldwell, D.G., Tsagarakis, N.G.: Translating videos to commands for robotic manipulation with deep recurrent neural networks. In: 2018 ICRA. IEEE (2018)
Sun, S.H., Noh, H., Somasundaram, S., Lim, J.: Neural program synthesis from diverse demonstration videos. In: ICML, pp. 4797–4806 (2018)
Pastra, K., Aloimonos, Y.: The minimalist grammar of action. Philos. Trans. Roy. Soc. B Biol. Sci. 367(1585), 103–117 (2012)
Chomsky, N.: Lectures on Government and Binding: The Pisa Lectures, no. 9. Walter de Gruyter, Berlin (1993)
Ryoo, M.S., Aggarwal, J.K.: Semantic representation and recognition of continued and recursive human activities. Int. J. Comput. Vis. 82(1), 1–24 (2009). https://doi.org/10.1007/s11263-008-0181-1
Ryoo, M., Aggarwal, J.: Stochastic representation and recognition of high-level group activities. Int. J. Comput. Vis. 93(2), 183–200 (2011). https://doi.org/10.1007/s11263-010-0355-5
Summers-Stay, D., Teo, C.L., Yang, Y., Fermüller, C., Aloimonos, Y.: Using a minimal action grammar for activity understanding in the real world. In: IROS, pp. 4104–4111. IEEE (2012)
Shu, T., Gao, X., Ryoo, M.S., Zhu, S.: Learning social affordance grammar from videos: transferring human interactions to human-robot interactions. In: 2017 IEEE International Conference on Robotics and Automation, ICRA, pp. 1669–1676 (2017)
Koppula, H., Saxena, A.: Learning spatio-temporal structure from RGB-D videos for human activity detection and anticipation. In: ICML, pp. 792–800 (2013)
Koppula, H.S., Gupta, R., Saxena, A.: Learning human activities and object affordances from RGB-D videos. IJRR 32(8), 951–970 (2013)
Amor, H.B., Neumann, G., Kamthe, S., Kroemer, O., Peters, J.: Interaction primitives for human-robot cooperation tasks. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2831–2837. IEEE (2014)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Chelba, C., et al.: One billion word benchmark for measuring progress in statistical language modeling. arXiv preprint arXiv:1312.3005 (2013)
Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: surprisingly easy synthesis for instance detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1301–1310 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, H., Lai, PJ., Paul, S., Kothawade, S., Nikolaidis, S. (2022). Learning Collaborative Action Plans from YouTube Videos. In: Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (eds) Robotics Research. ISRR 2019. Springer Proceedings in Advanced Robotics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-95459-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-95459-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95458-1
Online ISBN: 978-3-030-95459-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)